/ All data relating to a mcmc run.
attrs: backend: "stan" or "pymc3" version: str model_name?: str comment?: str model_version?: str timestamp: int author?: str
/coords For each dimension, we store a corresponding variable here, that contains the labels for that dimension.
/model Each backend can store a representation of the model in here. (I guess source code for stan, no clue for pymc3 at this point.)
/sample_stats Statistics computed while sampling, like step size (step_size), depth, diverging, energy (for HMC), log probability (lp), and log likelihood (log_likelihood).
/initial_point One point in parameter space for each chain, where that chain started. TODO We could also store that as an attribute to each var in /trace
/tuning_trace? Same as /trace, but optional. It contains the trace during tuning. Optional. (only if store_tune=True or so?)
/tuning_advi? Some data about an initialization advi run? History of convergence stats, final sd and mu params? Optional
/divergences Points in parameter space where the leapfrog starts that lead to a divergence (excluding tuning). Does stan have access to that info? We could also just store the accepted point of a divergent trajectory.
/observed_data All data that is used in observed variables (or the data (or transformed?) data sections in stan.)
/warnings A list of warnings during sampling. Eg low effective_n, divergences.... TODO Not sure about the format. Can we somehow share at least part of that between stan/pymc? They mostly produce the same warnings I think.
/prior? Samples from the prior distribution. Same shapes as in trace. (except for (sample, chain))
/prior_predictive? Samples from the prior predictive distribution. Same vars as in /data
/posterior_predictive? Samples from the posterior predicitve distribution. Same vars as in /data
/trace TODO We could call this /posterior
attrs:
The final parameters for the sampler. ie the final mass matrix and step size.
/trace//var1
One entry for each variable. The first two dimensions should always be
`(chain, sample)`. I guess the decision whether or not we want to expose a stacked version `draw=('chain', 'sample')`
is up to arviz.
Variable names must not share names with coordinate names.
attrs:
is_free: Whether or not the variable is a free variable for the sampler, or a transformed one
domain: One of (“reals”, “pos-reals”, “integers”, “sym-pos-def”, "interval"...)
TODO For stuff like sym-pos-def we need to know along which dims it is a matrix.
TODO This data could also be stored in /model
sym_pos_axis: [dim_idx1, dim_idx2]
interval_lower:
interval_upper
TODO How would this deal with cases where the lower and upper bounds depend on the index?
TODO: In order to reproduce the run, it may make sense to also store some data on the random state (in numpy, this is a tuple of arrays), as
well as some version info. Hopefully just PyMC3, (3, 4, 1) or similar works.